Senior AI/ML Engineer

Job requirements

Experience Range: 2 to 4 years of experience, including at least 2 years specifically focused on developing LLM-based applications, RAG systems, or AI agent workflows Key Responsibilities:

Design and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and reliable solutions

Develop and optimize Retrieval-Augmented Generation (RAG) pipelines by implementing chunking strategies, embedding models, retrieval ranking, and context window management for precise information retrieval

Build and iterate on prompt engineering layers, systematically testing and refining prompts and chain-of-thought strategies to deliver consistent outputs across diverse inputs

Implement tool orchestration within agent workflows, integrating agents with databases, rule engines, validation systems, and formatting tools for seamless operation

Establish automated quality checks and validation layers to proactively identify and resolve issues before outputs reach human reviewers

Collaborate with Data Scientists to instrument solutions for measurement, developing evaluation frameworks and tracking solution performance against defined targets

Deploy, monitor, and maintain AI/ML solutions in production environments, ensuring reliability, scalability, and robust error handling

Design and implement feedback loops to capture expert review data and translate it into measurable improvements in agent performance

Required Skills:

Advanced proficiency in Python

Hands-on experience with LLM frameworks such as LangChain or LlamaIndex

Expertise in prompt engineering for systematic testing and iteration

Deep understanding of RAG architectures including embedding models, vector stores, retrieval strategies, and re-ranking

Experience building multi-step agent workflows with tool use and branching logic

Experience deploying and maintaining AI/ML solutions in production environments

Experience with data pipeline development for feeding AI systems

Preferred Skills:

Experience with multi-agent orchestration frameworks

Background in content generation, translation, or document processing solutions

Familiarity with feedback loops, RLHF, or reward model training

Knowledge of multi-modal AI systems including voice-to-text, document understanding, and image analysis

Experience with evaluation frameworks for generative AI and automated scoring

Experience with LLM cost optimization strategies such as model routing, caching, and prompt compression

Desired Qualifications:

Bachelor's degree in Computer Science, Data Science, Information Technology, Statistics, or a closely related discipline

Certification in Machine Learning or Artificial Intelligence (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty)

Certification in LLM engineering or generative AI (e.g., DeepLearning.AI Generative AI with LLMs)